Audience

• Researchers in academic & industrial laboratories who make active use of optical microscopes, and desire to make image-based quantitative measurements for hypothesis testing, screening, and modeling
• Particular emphasis is placed on fluorescence based multi-dimensional microscopy (3-D confocal/multi-photon, time-lapse, multi-channel, etc.)
• This course will provide instruction on biological image analysis concepts/theory, and hands-on practical learning.

Learning Objectives

• Upon successful completion of this course, the participants will have a working knowledge of basic open-source computational image analysis tools (FARSIGHT, ITK, VTK, Paraview, Overview) to generate quantitative measurements from 3D/4D/5D microscopy image data.

• Developing skills on the use of a selected subset of computational methods from the ITK and FARSight libraries.

• Developing awareness of more advanced tools that are part of the ITK and FARSight libraries, as well as the support resources available such as email forums and Wiki pages.

• Specifically,
  • Ability to read, visualize and write images files for various common formats.
  • Ability to design computational scripts for image pre-processing to correct for common imaging artifacts (noise, blur, spectral overlap).
  • Being able to segment common types of biological objects (cell, nuclei, membranes, micro-vasculature, neurites, foci) in 2D and 3D images of cells and tissue.
  • Being able to perform edit-based validation of segmentation results.
  • Being able to perform registration and montaging.
  • Being able to perform cell and organelle tracking, and analysis of changes in time-lapse image series.
  • Being able to compute morphological measurements of biological objects.
  • Being able to compute associative measurements linking two or more biological objects.
  • Being able to summarize and conduct statistical analysis of measurements.

Pre-requisites

• Familiarity with contemporary computers PC/Mac/Linux
• Basic knowledge of programming using scripting languages
  • We will use Python
• Familiarity with common viewing tools and file formats

Content

Methodology